MRes Ioanna Kotari
During my undergraduate studies at the University of Crete in Greece, I specialised in Environmental Biology and the management of natural resources. I completed my thesis on paralogous gene evolution and expression and I continued on an internship at University College London, where I dove into learning how to use bioinformatics skills to study substitution models in deep phylogenies. This internship sparked my interest in developing more computational skills to handle evolutionary questions and led me to obtain an MRES degree on “Computational methods in ecology and evolution” at Imperial College London. My master’s thesis focused on developing a deep learning algorithm to distinguish between hard and soft selective sweeps from low coverage sequencing data of non-model organisms.
During my PhD project, I will be expanding on polymorphism-aware phylogenetic models (PoMos) by introducing PoMo-cod, a codon substitution model for detecting signatures of natural selection on protein-coding genes. PoMo-cod will allow to disentangle the effects of natural selection from known confounding forces (such as mutational biases, demography and GC-biased gene conversion) in determining the genetic diversity of multiple populations and species, thus producing more accurate genome-wide maps of diversifying evolving genes.
Diogo da Silva Ribeiro, MSc.
I obtained my Bachelor's degree in Biology and a Master's in Bioinformatics and
Computational Biology at Porto University, Portugal. During my Master's thesis, I developed
methods to quantify the degree of phylogenetic signal while accounting for tree uncertainty. I
focused on categorical traits for which fewer methods exist. My Master's thesis sparked my
interest in developing computational methods to uncover biological signatures of adaptation
from genomic data, leading me to an internship at the Institute of Population Genetics.
Currently, I am applying Machine Learning techniques to analyze time series data of allele
frequency changes in fruit flies that have been experimentally evolved for hundreds of
generations. The primary objective is to detect the number of selection targets and their
selection coefficients as a way to characterize the complex adaptive architecture of
temperature adaptation. Additionally, I am implementing Machine Learning techniques to
accelerate Bayesian phylogenetic inference, which will permit the scaling up of parameter
estimation in complex evolutionary scenarios, such as the evolution of coding sequences.